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      DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images

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          Abstract

          Single-particle cryo-electron microscopy (cryo-EM) is a technique for biomolecular structure reconstruction from vitrified samples containing many copies of a biomolecular complex (known as single particles) at random unknown 3D orientations and positions. Cryo-EM allows reconstructing multiple conformations of the complexes from images of the same sample, which usually requires many rounds of 2D and 3D classifications to disentangle and interpret the combined conformational, orientational, and translational heterogeneity. The elucidation of different conformations is the key to understand molecular mechanisms behind the biological functions of the complexes and the key to novel drug discovery. Continuous conformational heterogeneity, due to gradual conformational transitions giving raise to many intermediate conformational states of the complexes, is both an obstacle for high-resolution 3D reconstruction of the conformational states and an opportunity to obtain information about multiple coexisting conformational states at once. HEMNMA method, specifically developed for analyzing continuous conformational heterogeneity in cryo-EM, determines the conformation, orientation, and position of the complex in each single particle image by image analysis using normal modes (the motion directions simulated for a given atomic structure or EM map), which in turn allows determining the full conformational space of the complex but at the price of high computational cost. In this article, we present a new method, referred to as DeepHEMNMA, which speeds up HEMNMA by combining it with a residual neural network (ResNet) based deep learning approach. The performance of DeepHEMNMA is shown using synthetic and experimental single particle images.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination

            A software tool, cryoSPARC, addresses the speed bottleneck in cryo-EM image processing, enabling automated macromolecular structure determination in hours on a desktop computer without requiring a starting model.
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              Adam: A Method for Stochastic Optimization

              We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015
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                Author and article information

                Contributors
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                08 September 2022
                2022
                : 9
                : 965645
                Affiliations
                IMPMC - UMR 7590 CNRS , Sorbonne Université , MNHN , Paris, France
                Author notes

                Edited by: Olga S. Sokolova, Lomonosov Moscow State University, Russia

                Reviewed by: Antonio Trovato, University of Padua, Italy

                Dimitry Tegunov, Genentech, Inc., United States

                *Correspondence: Slavica Jonic, slavica.jonic@ 123456upmc.fr

                This article was submitted to Structural Biology, a section of the journal Frontiers in Molecular Biosciences

                Article
                965645
                10.3389/fmolb.2022.965645
                9493108
                36158571
                aa819a06-9ece-41b6-96f3-d4346f085fcc
                Copyright © 2022 Hamitouche and Jonic.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 June 2022
                : 08 August 2022
                Funding
                Funded by: Agence Nationale de la Recherche , doi 10.13039/501100001665;
                Categories
                Molecular Biosciences
                Original Research

                deep learning,cryo-em single particle analysis,molecular flexibility,continuous conformational heterogeneity,normal mode analysis,hemnma,deephemnma

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